CL3.1.8

Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (“control”) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).

For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76°C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5σ), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.

Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model’s natural variability estimates are used.

Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.

Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.

Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science. Nat Clim Change 9(3), pp. 180-182.

How to cite: Sippel, S., Meinshausen, N., Székely, E., Fischer, E., Pendergrass, A. G., Lehner, F., and Knutti, R.: Robust detection of forced warming in the presence of potentially large climate variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9611, https://doi.org/10.5194/egusphere-egu21-9611, 2021.

The observed increase of global air surface temperature (GSAT) has long been attributed to human activities. However, updating estimates of human-induced changes, and changes induced by specific subsets of forcings (e.g., green-house gases) remains of high interest to better understand recent changes and also produce refined projections.

Here, we use the newest climate model ensemble (CMIP6), improved observations, and a new statistical method to narrow uncertainty on the response to historical forcings. In addition, we focus on estimating the total warming since the pre-industrial (using 1850-1900 as a reference baseline), while most previous studies considered shorter periods.

Results suggest that most of the observed warming since the pre-industrial (+1.22°C +/-0.15°C in 2020) is human-induced (+1.15°C +/-0.15°C) and that a substantial fraction of GHG-induced warming (+1.54°C +/-0.33°C) has been offset by other anthropogenic factors (-0.39°C +/-0.28°C). We also quantify the contribution of specific forcings to the 2010-2019 warming rate, suggesting that the current rate of human-induced warming is +0.22°C/decade (+/-0.05°C/decade). We then derive implications of these findings in terms of future climate change, i.e., the response to a range of scenarios. Our results suggest that historical observations and historical climate change are already very informative about future changes and the property of the Earth System in general.

How to cite: Ribes, A., Qasmi, S., and Gillett, N.: Updated attribution of GSAT changes and implications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16112, https://doi.org/10.5194/egusphere-egu21-16112, 2021.

As the climate becomes warmer under the influence of anthropogenic forcings, increases in the concentration of the atmospheric water vapour may lead to an intensification of wet and dry extremes. Understanding regional hydroclimatic changes can provide actionable information to help communities adapt to impacts specific to their location. This study employs an ensemble of 9 CMIP6 models and compares experiments with and without the effect of human influence using detection and attribution methodologies. The analysis employs two popular drought indices: the rainfall-based standardised precipitation index (SPI), and its extension, the standardized precipitation evapotranspiration index (SPEI), which also accounts for changes in potential evapotranspiration. Both indices are defined relative to the pre-industrial climate, which enables a comparison between past, present and future climatic conditions. Potential evapotranspiration is computed with the simple, temperature-based, Thornthwaite formula. The latter has been criticised for omitting the influences of radiation, humidity and wind, but has been shown to yield very similar trends, spatial averages and correlations with more sophisticated models. It is therefore deemed to be adequate in studies assessing the broader overall effect of climate change, which are more concerned with wet and dry trends and changes in characteristics of extremes rather than the precise estimation of drought index values. The rainfall-based index suggests a shift towards wetter conditions in the north and dryer in the south of the continent, as well as an overall increase in variability. Nevertheless, when the temperature effect is included, the wet trends in the north are largely masked leading to increasingly drier summers across most of the continent. A formal statistical methodology indicates that the fingerprint of forced climate change has emerged above variability and is thus detectable in the observational trends of both indices. A broadening of the SPI distribution also suggests higher rainfall variability in a warmer climate. The study demonstrates a striking drying trend in the Mediterranean region, suggesting that what were extremely dry conditions there in the pre-industrial climate may become normal by the end of the century.

How to cite: Christidis, N. and Stott, P.: The effect of human influence on wet and dry European summers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3110, https://doi.org/10.5194/egusphere-egu21-3110, 2021.

The impact of climate change observed in recent decades can be noticed in the structure of precipitation. The increasing amount of periods without rainfall, decreasing annual snowfall totals, and shortening the duration of snow cover significantly affect water resources and the intensity of a number of environmental processes, such as soil erosion by water.

The main aim of this paper is to determine the structure of rainfall in years 1987-2020, based on series of meteorological measurements in the Parsęta Base Station of Integrated Monitoring of Natural Environment at Storkowo in Drawskie Lakeland (NW Poland). The analyzes included precipitation amounts, number of days with precipitation, rainfall intensity, kinetic energy and erosivity and several rainfall indices. During this period, there is observed a significant increase of air temperature, which equals 0.47°C for 10 years. In the case of precipitation, a small increasing trend is marked statistically insignificant. The average annual precipitation was 698.6 mm, whilst precipitation in the winter half-year equalled 41.2% of total and 58.8% in the summer half-year. The annual rainfall erosivity, calculated according to Wishmeier and Smith’s formula, changed from 144.7 to 782.1 MJmm/ha/h, while Modified Fournier Index (MFI) ranged from 53.8 mm to 119.0 mm and was not statistically significant.

The analysis of precipitation with different daily totals did not show a significant increase in the share of precipitation with higher values. The relative precipitation index (RPI) showed no increase in the number of dry months of a year. Moreover, the analysis of occurrence of periods of light droughts, dry spells and droughts does not indicate any significant increase in the number and frequency of such events. On the other hand, a similar analysis of vegetation period (April-September) shows statistically insignificant trend of decrease in the number and frequency of precipitation less series. Another indicator important for the assessment of water conditions, the Sielianinov hydrothermal coefficient was calculated for period April-October, and showed lack of long-term trend changes in the observed period.

The water shortages in the upper Parsęta catchment observed in recent years are probably the result of decrease in the contribution of snow in the precipitation structure and a significant reduction in the number of days with snow cover. This limits the underground retention and surface outflow and has an impact on the functioning of biotic environment and agriculture.

How to cite: Majewski, M., Szpikowski, J., Domańska, M., and Szpikowska, G.: The structure of precipitation and rainfall erosivity in the upper Parsęta catchment (Drawskie Lakeland, NW Poland) in 1987-2020 as an indicator of climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8186, https://doi.org/10.5194/egusphere-egu21-8186, 2021.

Stratospheric zonal winds are disturbed by tropospheric forced planetary waves which modulate the quasi-biennial oscillation (QBO) in the northern hemisphere during winter. QBO is the quasi periodic oscillation of zonal winds in the lower stratosphere with an average recurrence of 28 months. QBO is mainly characterized by zonal mean circulation in the equatorial and low latitudes of middle atmosphere. Investigations indicate that although QBO is an equatorial oscillation there is a strong correlation between QBO and stratospheric polar wind patterns. Additionally, westerly and easterly phases of QBO alter the strength of these winds differently. During the westerly phase of QBO, northern stratospheric zonal winds are stronger whereas the easterly phase coincides with the weaker stratospheric zonal winds.

In this study, easterly and westerly zonal winds at 30hPa for the latitudes between 5°S and 5°N which characterize the westerly (QBO-W) and easterly (QBO-E) phases of the QBO is examined using CMIP5 MPI-ESM-MR RCP4.5 scenario for the years between 2006 and 2099 for winter. It is found that climatic changes in the zonally asymmetric zonal wind characteristics in both phases of QBO modulates the polar stratospheric zonal winds differently. A prominent wave-1 structure in QBO-E phase and a wave-2 structure in QBO-W phase are apparent and effect the strength of the polar stratospheric zonal winds.

This study is a supported by TUBİTAK (The Scientific and Technology Research Council of Turkey), The Scientific and Technological Research Projects Funding Program, 1001.The project number is 117Y327.

How to cite: Ozturk, R., Demirhan, D., Unal, Y., and Topcu, S.: Influence of Different Phases of Quasi-Biennial Oscillation on the Evolution of Polar Stratospheric Zonal Winds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8375, https://doi.org/10.5194/egusphere-egu21-8375, 2021.

How to cite: Gopika, S., Suresh, I., Lengaigne, M., Vialard, J., and Izumo, T.: Emergence time of regional signals in tropical rainfall and sea surface temperature in CMIP5/6 simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11183, https://doi.org/10.5194/egusphere-egu21-11183, 2021.

Tourism is a major socioeconomic contributor to established and emerging destinations in the Mediterranean region. Recent studies introducing the Holiday Climate Index (HCI) highlight the significance of climate as a factor in sustaining the competitiveness of coastal and urban destinations. The aim of this study is to assess the future HCI performances of urban and beach destinations in the greater Mediterranean region. For this purpose, HCI scores for the reference (1971-2000) and future (2021-2050, 2070-2099) periods were computed with the use of two latest greenhouse gas concentration trajectories, RCP 4.5 and 8.5, based on the Middle East North Africa (MENA) Coordinated Regional Downscaling Experiment (CORDEX) domain and data. The outputs were adjusted to a 500 m resolution via the use of lapse rate corrections that extrapolate the climate model topography against a resampled digital elevation model. All periodic results were seasonally aggregated and visualized on a (web) geographical information system (GIS). The web version of the GIS also allowed for a basic climate service where any user can search her/his place of interest overlaid with index ratings. Exposure levels are revealed at the macro scale while sensitivity is discussed through a validation of the climatic outputs against visitation data for one of Mediterranean's leading destinations, Antalya. HCI:Urban results showed that Canary Islands hold suitable conditions for tourism during almost all four seasons and all five periods which will have certain implications when other core Mediterranean competitors lose their relative climatic attractiveness. HCI:Beach results for the summer season showed that Las Canteras, Alicate, Pampelonne, Myrtos, Golden Sands and Edremit all pose Very Good to Excellent conditions without any Humidex risks for the extreme future scenario (2070-2099 RCP8.5).
Much detailed outputs of the study can be viewed from the web service at:
http://climatechange.boun.edu.tr/en/holiday-climatology-of-the-mediterranean/

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How to cite: Saygili Araci, F. S., Demiroglu, O. C., Pacal, A., Hall, C. M., and Kurnaz, M. L.: Future Holiday Climate Index (HCI) Performances of Urban and Beach Destinations in the Mediterranean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13217, https://doi.org/10.5194/egusphere-egu21-13217, 2021.

In order to better understand how climate changes have taken place in Iran, we carried out a comprehensive analysis of the spatio-temporal trends of various climate variables and extreme indices during 1986 to 2015 at the county-level across the country. Additionally, the interannual oscillation of the temperature and precipitation and their related extreme indices were examined throughout the research. In this study, ERA5-Land and AgrERA5 datasets with hourly, daily, and monthly temporal resolutions were aggregated to the county-level to calculate climate extreme indices. Subsequently, different approaches such as the original Mann-Kendall (MK) trend test, MK with block bootstrap modification, MK with variance correction modification, correlated seasonal MK (partial MK), original and seasonal Sen's Slope were implemented to detect the magnitude and the statistical significance of climatic trends for each county. Finally, the continuous wavelet transform was employed for whole country averages to investigate fluctuations and dominant periods of the variables and indices. The reanalysis model datasets offered us two advantages; firstly, it facilitates obtaining data in some regions with sparse weather stations and secondly, it allows us to inquire about some climate variables that were less studied in the literature, for instance, the wind speed, the surface air pressure, the solar radiation, the surface albedo, the runoff, the evaporation, and the skin reservoir content. The results showed a significant increasing trend in the temperature over all counties and a nonsignificant drying trend in the precipitation for almost the whole country. Other climate variables demonstrated more mixed spatio-temporal trends; however, generally, the wind speed and the solar radiation had an upward trend, the runoff, the skin reservoir content, and the surface albedo showed a downward trend, while the surface air pressure and the evaporation trends exhibited a great deal of variety. Furthermore, the hot climate extremes were increased throughout the country whereas the cold extremes and the extreme precipitations were quite in the opposite direction. It is noteworthy that the Continental and the Warm-Temperate climates were more vulnerable compare to the Arid and Semi-Arid Climates. At last, the wavelet power spectrum maps indicated the consistency between the temperature and precipitation and their related extremes and also showed a reduction in the fluctuation of the precipitation and a constant oscillation for temperature over the study period.

How to cite: Malaekeh, S., Safaie, A., and Shiva, L.: Spatio-temporal variations of climate variables and extreme indices over Iran during 1986-2015, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12126, https://doi.org/10.5194/egusphere-egu21-12126, 2021.

This research aims to support studies related to the adaptation capacity of the Amazon region to climate change. The Belo Monte Hydroelectric Power Plant (HPP) is in the Xingu River basin, in eastern Amazonia. Deforestation coupled with changes in water bodies that occurred in the drainage area of Belo Monte HPP over the past few decades can significantly influence the hydroclimatic features and, consequently, ecosystems and energy generation in the region. In this context, we analyze the climatology and trends of climate extremes in this area. The climate information comes from daily data in grid points of 0.25° x 0.25° for the period 1980-2013, available in http://careyking.com/data-downloads/. A set of 17 climate extremes indices based on daily data of maximum temperature (TX), minimum temperature (TN), and precipitation (PRCP) was calculated through the RClimDex software, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The Mann-Kendall and the Sen’s Curvature tests are used to assess the statistical significance and the magnitude of the trends, respectively. The drainage area of the Belo Monte HPP is dominated by two climatic types: an equatorial climate in the north-central portion of the basin, with high temperatures and little variation throughout the year (22°C to 32°C), in addition to more frequent precipitation; and a tropical climate in the south-central sector, which experiences slightly more pronounced temperature variations throughout the year (20°C to 33°C) and presents a more defined wet and dry periods. The south-central portion of the basin exhibits the highest temperature extremes, with the highest TX and the lowest TN of the year occurring in this area, both due to the predominant days of clear skies in the austral winter, as to the advance of intense masses of polar air at this period. The diurnal temperature range is lower in the north-central sector when compared to that in the south-central region since the first has greater cloud cover and a higher frequency of precipitation. The largest annual rainfall volumes are concentrated at the north and west sides (more than 1,800 mm) and the precipitation extremes are heterogeneous across the basin. The maximum number of consecutive dry days increases from the north (10 to 20 days) to the south (90 to 100 days). The annual frequency of warm days and nights is increasing significantly in a large part of the basin with a magnitude ranging predominantly from +7 to +19 days/decade. The annual rainfall shows a predominant elevation sign of up to +200 mm/decade only in the northern part of the basin, while the remainder shows a reduction of up to -100 mm/decade. The duration of drought periods increases in the south-central sector of the basin, reaching up to +13 days/decade in some areas. The results of this study will be used in the future as an important input, together with exposure, sensibility, and local adaptation capacity, to design adaptation strategies that are more consistent with local reality and to the needs of local communities.

How to cite: Luiz-Silva, W., Regoto, P., de Vasconcellos, C. F., Guimarães, F. B. F., and Garcia, K. C.: Climatology and Trends of Climate Extremes of Temperature and Precipitation in Belo Monte Hydropower Plant – Eastern Amazon, Brazil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9079, https://doi.org/10.5194/egusphere-egu21-9079, 2021.

This study carried out an updated detection and attribution analysis of extreme temperature changes for 1951-2015. Four extreme temperature indices (warm extremes: annual maximum daily maximum/minimum temperatures; cold extremes: annual minimum daily maximum/minimum temperatures) were used considering global, continental (6 domains), and subcontinental (33 domains) scales. HadEX3 observations were compared with CMIP6 multi-model simulations using an optimal fingerprinting technique. Response patterns of extreme indices (fingerprints) to anthropogenic (ANT), greenhouse gas (GHG), anthropogenic aerosol (AA), and natural (NAT) forcings were estimated from corresponding CMIP6 forced simulations. Pre-industrial control simulations (CTL) were also used to estimate the internal variability. Results from two-signal detection analysis where the observations are simultaneously regressed onto ANT and NAT fingerprints reveal that ANT signals are robustly detected in separation from NAT in global and most continental regions for all extreme indices. At subcontinental scale, ANT detection occurs especially in warm extremes (more than 60% of regions). Results from three-signal detection analysis where observations are simultaneously regressed onto GHG, AA, and NAT fingerprints show that GHG signals are detected and separated from other external forcings over global, most continental, and several subcontinental (more than 60%) domains in warm extremes. In addition, AA influences are jointly detected in warm extremes over global, Europe and Asia. The detected GHG forcings are found to explain most of the observed warming while AA forcings contribute to the observed cooling for the early decades over globe, Europe, and Asia with a slight warming over Europe during recent decades. Overall, improved detection occurs compared to previous studies, especially in cold extremes, which is due to the use of extended period which increases signal-to-noise ratios.

How to cite: Seong, M.-G., Min, S.-K., Kim, Y.-H., Zhang, X., and Sun, Y.: Greenhouse gas and aerosol contributions to the observed global and regional changes in extreme temperature changes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1930, https://doi.org/10.5194/egusphere-egu21-1930, 2021.

In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.

Keywords: climate change, temperature, extreme events, attribution, CMIP6

Acknowledgement: This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)

How to cite: Engdaw, M. M., Ballinger, A., Hegerl, G., and Steiner, A.: Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7137, https://doi.org/10.5194/egusphere-egu21-7137, 2021.

Extreme heat occurrence worldwide has increased in the past decades. Greenhouse gas emissions, if not abated aggressively, will lead to large increases in frequency and intensity of heat extremes. At the same time, many cities are facing severe air pollution problems featuring high‐PM episodes that last from days to weeks. Based on a high‐resolution decadal‐long model simulation using a state‐of‐the‐science regional chemistry‐climate model that is bias corrected against reanalysis, here we show that when daily average wet‐bulb temperature of 25 °C is taken as the threshold for severe health impacts, heat extremes frequency averaged over South Asia increases from 45 ± 5 days/year in 1997–2004 to 78 ± 3 days/year in 2046–2054 under RCP8.5 scenario. With daily averaged PM_2.5 surface concentration of 60 μg/m³ defined as the threshold for such “unhealthy” extremes, high‐PM extremes would occur 132 ± 8 days/year in the Decade 2050 under RCP8.5. Even more concerning, due to the potential health impacts of two stressors acting in tandem, is the joint occurrence of the heatwave and high‐PM hazard (HHH), which would have substantial increases of 175% in frequency and 79% in duration. This is in contrast to the 73–76% increase for heatwave or high PM when assessed individually. The fraction of land exposed to prolonged HHH increases by more than tenfold in 2050. The alarming increases in just a few decades pose great challenges to adaptation and call for more aggressive mitigation. For example, under a lower emission pathway, the frequency of HHH will only increase by 58% with a lower frequency of high‐PM extremes.

How to cite: Wu, X., Xu, Y., Kumar, R., Barth, M., Diao, C., Gao, M., Lin, L., Jones, B., and Meehl, G.: Substantial Increase in the Joint Occurrence and Human Exposure of Heatwave and High‐PM Hazards Over South Asia in the Mid‐21st Century, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3631, https://doi.org/10.5194/egusphere-egu21-3631, 2021.

Climate change significantly influences the global hydrological cycle and consequently affects climatic extremes. The present study is focussed upon varying patterns of climate extremes using observed daily precipitation (1989-2019), daily temperature from Global Meteorological Forcing Dataset (GMFD) (1985-2016) and simulated daily meteorological forcing data (2025-2055 and 2065-2095) of 21 GCMs attained from the statistically downscaled dataset, NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) under RCP4.5 and RCP8.5 scenario across India. The copula method was employed to estimate the joint return period based on different climate extreme indices. Here, we found that R20, R95p and CWD attain an increasing trend and CDD mostly shows a decreasing trend in major segments of country in future. Based upon the 10-year joint return periods (1989-2019), it is found that parts of north-western, north-eastern, southern, western region and Western Ghats are highly prone towards floods and a large portion of the country is susceptible to co-occurrence of floods and droughts. Moreover, the study shows that many regions with less vulnerability towards precipitation extremes would become more vulnerable in future. Furthermore, TXx, TNx, TX90p, TN90p, TNn and TXn are found to be significantly increasing in future except increasing during 2065-2095 under RCP4.5 predominantly across the country. And, TX10p and TN10p follows a significantly decreasing trend in future across the except exhibiting a decreasing trend during 2065-2095 under RCP4.5, throughout the country. With the projected increase in hot days/nights, the frequency of concurrence of extreme number of hot days (TX90p) and nights (TN90p) within a year would increase in the future across the country. The present study provides useful information on the regional distribution of climate extremes and how they might change in the future. This information can further contribute to facilitate an effective planning strategy to improve resilience towards climate extremes.

How to cite: Kumar, N., Kumar Goyal, M., Kumar Gupta, A., Jha, S., Das, J., and A. Madramootoo, C.: Copula based Assessment of Climate Extremes across India: Past and Future, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9993, https://doi.org/10.5194/egusphere-egu21-9993, 2021.

August 2020 set a new record high sea surface temperature (SST) in the northwestern Pacific (NWPac; 120°E–180°E, 20°N–35°N). This anomalous condition potentially intensified tropical cyclones such as Typhoon Haishen, causing severe damage to the Korean Peninsula and Japan. Although the NWPac Ocean has gradually warmed due to human-induced greenhouse gas emissions since the mid‐20th century, the extent to which anthropogenic climate changes increase the occurrence likelihood of such regionally unprecedented warm SSTs is unclear yet. Here we analyzed the historical and SSP2-4.5 scenario simulations of CMIP6 and DAMIP as well as observational datasets. Our results show that owing to historical anthropogenic forcing, the occurrence probability of the 2020 record-warm NWPac SST is increased from once-in-1000 years to about once-in-15 years in 2001-2020. As warming caused by greenhouse gases was largely canceled by aerosol cooling, anthropogenic effects on the NWPac SST were not distinguishable from internal variability in the 20th century. The SSP2-4.5 scenario simulations also indicate that the 2020 record-warm SST is becoming a new normal climate condition of August by 2031–2050, or once the global air temperature above preindustrial level exceeds 1.5°C.

How to cite: Hayashi, M., Shiogama, H., Emori, S., Ogura, T., and Hirota, N.: Detecting anthropogenic effects on the record-warm northwestern Pacific sea surface temperature in August 2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6925, https://doi.org/10.5194/egusphere-egu21-6925, 2021.

Multiple climate extremes have diverse impacts on the human environment and the natural ecosystem. The use of a compact set of climate change indices enhances our understanding of the combined impacts of extreme climatic conditions. In this study, we calculated percentile based extreme temperature and precipitation indices, and self-calibrating Palmer Drought Severity Index over the Europe-Mediterranean (EURO-MED) region for 1979-2016 period. Moreover, we extended the Climate Extremes Index (CEI) as the modified Climate Extremes Index (mCEI) to obtain combined information regarding the extremes on the grid basis. As a holistic approach mCEI provides detailed spatiotemporal information on annual timescale, and high-resolution grid-based data allows us to do detailed country-based and city-based analyses. For temperature, we use the last generation ERA5 reanalysis dataset, and for precipitation, we use MSWEP gridded observational dataset. The results indicate that warm temperature extremes are significantly on the rise over the EURO-MED region whereas the cold temperature extremes decrease. The extreme drought has a significant increasing trend. Although there are heterogeneous regional distributions, extreme precipitation indices have a significant increasing tendency. Additionally, we found that the Mediterranean coasts, the Balkan countries, the Eastern Europe, Iceland, the parts of western Russia, the parts of Turkey, and the parts of Syria and Iraq are the major hot-spots for the combined extremes based on mCEI. Among the major urban agglomerations of the EURO-MED region, 28 cities exhibit a significant increasing trend of the mCEI greater than 1.5% decade^-1. These results agree with the previous findings related to the climatic extremes of the EURO-MED climate hotspot, and strengthen the findings on human-induced climate change.

How to cite: Kelebek, M. B., Batıbeniz, F., and Önol, B.: CHANGES IN COMBINED EFFECT OF EXTREMES BASED ON MODIFIED CLIMATE EXTREMES INDEX (mCEI) OVER THE EUROPE-MEDITERRANEAN REGION, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12087, https://doi.org/10.5194/egusphere-egu21-12087, 2021.

How to cite: Riebold, J., Richling, A., Rust, H., Ulbrich, U., and Handorf, D.: Attributing temperature extremes in the Euro-Atlantic region to Arctic Sea Ice changes using a framework of conditional extreme event attribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13560, https://doi.org/10.5194/egusphere-egu21-13560, 2021.

Research Objective: Single-model initial condition large ensembles provide novel opportunities to study the physical drivers and risks of large-scale climate extremes in a changing climate. The probability of extremes such as weekly heatwaves, here quantified as seven-day maximum temperature (Tx7d), are usually approximated with a general extreme value distribution GEV  that is stationary or accounts for non-stationarity of a warming climate. However, estimating the occurrence probability of very rare climate extremes in the presence of large internal variability further benefits from the integration of process-based covariates characterising the preceding and concurrent climate conditions both at global and local scale.

Data & Methods: We here use more than 6000 years of stationary pre-industrial and 2xCO2 control simulations and an ensemble of 84 transient historical and RCP8.5 simulations performed with the Community Earth System Model CESM1.2 to develop and robustly test methods of quantifying extreme events under a broad range of climatic conditions. The generalised extreme value distribution is parametrised such that it can account for changing environmental circumstances, ranging from large-scale thermodynamic non-stationarity due to climate change, regional-scale dynamic forcing such as atmospheric blocking, or local land-surface conditions such as soil moisture deficits. Fields of covariates are integrated using approaches from statistical learning theory, accounting for the spatio-temporal correlation inherent in climate data.

Preliminary results: Dynamical forcing patterns as simulated by the earth system model compare well with those obtained from reanalysis data and inform the statistical model in a physically traceable fashion. How well the latter generalises is tested with respect to further simulations of the US CLIVAR Working Group on Large Ensembles. The relevance of different covariates can inform both detection and attribution as well as risk assessment how their respective statistical models can be further refined to account for the influence of physical drivers under present and future climate conditions.

How to cite: Zeder, J. and Fischer, E. M.: Towards a conditional representation of heat wave probability in large ensemble climate model data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7045, https://doi.org/10.5194/egusphere-egu21-7045, 2021.

Modified frequencies and magnitudes of extreme events due to climate change can have large impacts on societies and are therefore a key area of current research. Large model ensembles are required to quantify and attribute changes to extreme events. Until now, the large ensembles used for such studies are commonly atmosphere-only models forced with time-varying sea surface temperatures (SST) and sea ice. 

This approach is very powerful but presents problems of internal physical consistency. In an SST-forced model, the ocean acts as having an infinite heat capacity whereas in the real-world SST emerges dynamically from the interaction of atmospheric and oceanic processes (Dong et al., 2020). This is particularly relevant for the North Atlantic where ocean processes, in particular meridional heat transport, are key drivers of air-sea coupling. A long-standing challenge, however, is the computational cost of spinning up fully coupled atmosphere-ocean models that hinders their application to large-ensemble, high-resolution simulations required to quantify changing hazard frequencies of low-probability events.  

In this work we combine the HadAM4 (Webb et al., 2001) atmospheric model at N144 resolution with a Slab ocean (Hewitt & Mitchell, 1997; Williams et al., 2003), which includes a simple sea ice model, to yield the atmosphere-Slab Ocean model HadSM4. The Slab Ocean is forced with diagnosed heat convergence (Q-Flux) and surface currents for sea ice advection (a useful model-development finding for this kind of experiment is that including sea ice velocity information from reanalyses in the surface current field yields a substantially improved spatial pattern of sea ice). We are therefore able to directly compare SST-forced atmosphere-only runs with Q-Flux-forced runs where SST is an emergent property of the model, specifically accounting for the passive response of SSTs in the North Atlantic. Using the distributed infrastructure of climateprediction.net (Guillod et al., 2017; Massey et al., 2015) we run large ensembles to compare extreme statistics and quantify the importance of fast ocean-atmosphere coupling for extreme event statistics.

We further use this large ensemble setup to investigate the dynamics that drive extreme events from the ocean through air-sea interaction to atmospheric processes. We address is whether and how the slope of a return-time plot (related to the scale parameter of a GEV distribution) is affected by atmosphere-ocean interactions, since this statistic plays a central role in determining relative-risk estimates in event attribution studies. We then investigate how a perturbation to the Q-Flux, representing a change in ocean heat transport, propagates through the system and alters the statistics of extreme events.

Dong et al., 2020, Climate Dynamics, 55(5–6), 1225–1245. 

Guillod et al., 2017, Geoscientific Model Development, 10(5), 1849–1872. 

Hewitt & Mitchell, 1997, Climate Dynamics, 13(11), 821–834.

Massey et al., 2015, Quarterly Journal of the Royal Meteorological Society, 141(690), 1528–1545. 

Webb et al., 2001, Climate Dynamics, 17(12), 905–922. 

Williams et al., 2003, Climate Dynamics, 20(7–8), 705–721. 

How to cite: Aengenheyster, M., Sparrow, S., Watson, P., Wallom, D., Zanna, L., and Allen, M.: Impact of sub-seasonal atmosphere-ocean interactions on extreme event statistics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15649, https://doi.org/10.5194/egusphere-egu21-15649, 2021.

Extreme weather events are generally associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. On the other hand, the thermodynamic aspects of climate change are already largely apparent from observations, and are far more certain since they are anchored in agreed-upon physical understanding. The storyline method of extreme event attribution, which has been gaining traction in recent years, quantitatively estimates the magnitude of thermodynamic aspects of climate change, given the dynamical conditions. There are different ways of imposing the dynamical conditions. Here we present and evaluate a method where the dynamical conditions are enforced through global spectral nudging towards reanalysis data of the large-scale vorticity and divergence in the free atmosphere, leaving the lower atmosphere free to respond. We simulate the historical extreme weather event twice: first in the world as we know it, with the events occurring on a background of a changing climate, and second in a ‘counterfactual’ world, where the background is held fixed over the past century. We describe the methodology in detail, and present results for the European 2003 heatwave and the Russian 2010 heatwave as a proof of concept. These show that the conditional attribution can be performed with a high signal-to-noise ratio on daily timescales and at local spatial scales. Our methodology is thus potentially highly useful for realistic stress testing of resilience strategies for climate impacts, when coupled to an impact model.

How to cite: van Garderen, L., Feser, F., and Shepherd, T. G.: A Methodology for Attributing the Role of Climate Change in Extreme Events: A Global Spectrally Nudged Storyline, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3, https://doi.org/10.5194/egusphere-egu21-3, 2021.

We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP)
models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that
both the probability and the intensity of that event have increased significantly in response to human influence.
Remarkably, we find that the heat wave considered might not have been possible without climate change. Our
results also suggest that combining model data with observations can improve the description of hot temperature
distribution.

How to cite: Robin, Y. and Ribes, A.: How to combine climate models and observations in event attribution?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14636, https://doi.org/10.5194/egusphere-egu21-14636, 2021.

In response to the occurrence of large wildfire events across both hemispheres in recent years, the effort to understand the extent to which climate change may be altering the frequency of fire-conducive meteorological conditions has become an emerging subfield of attribution science. However, to date, the relative paucity of wildfire attribution studies, coupled with limited observational records, makes it difficult to draw solid and collective conclusions to better inform forest management strategies. The inter-study differences that emerge due to the choice of methodology and event definition are common to many attribution studies; for wildfire attribution in particular, the lack of consensus on how fire danger should be defined in a meteorological context presents an additional challenge.

Here, we present a framework for the simultaneous attribution of multiple extreme fire weather episodes of using an empirical-statistical methodology. Key to this framework is the development of a common spatiotemporal definition for extreme fire weather events. With reference to the fourth version of Global Fire Emissions Dataset (GFED4), we focus on all parts of the world that have experienced fires during the period 1995-2016. At each target grid point, we fit a Generalized Extreme Value (GEV) distribution, scaled by global mean surface temperature (smoothed over 4 years), to the annual maxima of a series of reanalysis-derived fire danger indicators (including the fire weather index) for the period 1980-2018. Using global maps of risk ratios and percentage of changes, we quantify the influence of recent global warming on the frequency and magnitude of fire weather extremes according to a common ‘event type’ definition, irrespective of their spatiotemporal occurrence. We subsequently conduct a collective attribution analysis of a series of recent exceptional events. We conclude with suggestions for further application to climate model ensembles and a discussion of the potential of our findings to inform decision-makers and practitioners.

How to cite: Liu, Z., Eden, J., Dieppois, B., and Blackett, M.: Development of a common definition approach for multi-event attribution of fire weather extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12487, https://doi.org/10.5194/egusphere-egu21-12487, 2021.

The interest in statements about the impact of climate change on a specific extreme weather or climate event is largest in the immediate aftermath of an event. The wider public and other stakeholder would like to know, ideally within days after an event occurred, if and how anthropogenic climate change has altered the frequency and severity of such an extreme event. While the scientific area of event attribution has developed quickly within the last decade, providing attribution statements shortly after the event is still a challenge.

To satisfy the public’s need for information, several groups are currently working towards a near real-time attribution system. As part of module B1.2 of the German ClimXtreme project, we are working on a prototype for a semi-automated attribution system to analyse extreme events affecting Germany. Initially, the focus is on heat waves, droughts, and extreme precipitation events, which have large impacts in Germany.

This attribution system will implement existing methodologies for the probabilistic event attribution and extend them, where required. Collaborations with international colleagues facilitate an ongoing exchange with the growing community specialising in extreme event attribution. A close collaboration with project partners within ClimXtreme will enable us to implement new methodologies from other modules of the ClimXtreme project.

In this presentation, we will give an overview of the scientific and technical approach, as well as the different methodologies that will be part of the prototype attribution system. We will also compare the methodologies and discuss their different benefits.

How to cite: Tradowsky, J., Lorenz, P., Kreienkamp, F., and Skålevåg, A.: Work towards the near real-time attribution of extreme weather and climate events at the German weather service (Deutscher Wetterdienst), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5727, https://doi.org/10.5194/egusphere-egu21-5727, 2021.